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Communicating Confidence in the Reliability of Micro- and Nanoplastic Identification in Human Health Studies

Environment & Health 2026 2 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Kevin Thomas, Susanne Belz, Andy M. Booth, Martin J. D. Clift, Richard K. Cross, Grace Davies, Hubert Dirven, Hubert Dirven, Sarah A. Dunlop, Alessio Gomiero, Shaowei Guo, Dorte Herzke, Koelmans Albert A., Ian Mudway, Elvis D. Okoffo, Cassandra Rauert, Saer Samanipour, Christos Symeonides, David Walker, Tingting Wang, Stephanie Wright, Jun-Li Xu, Jun-Li Xu, Leon Barron

Summary

This paper proposes a framework for improving confidence in how scientists identify and measure micro- and nanoplastics in human tissues and body fluids. The authors argue that studies need to use multiple complementary analysis methods and clearly report their limitations to produce reliable data. Better standardization in detection methods is critical for accurately assessing how much microplastic is actually inside people's bodies and what health risks it may pose.

Accurately quantifying and characterizing human internal exposure to micro- and nanoplastics are critical for assessing potential health risks. However, the detection of these particles in human tissues, fluids, cell systems, and relevant models remains a major analytical challenge. There is an urgent need for robust, selective, sensitive, and high-throughput methods capable of generating reliable quantitative data. Equally essential is the transparent reporting of methodological limitations and uncertainties, supported by rigorous data collection and standardized practices. These challenges are compounded by the ubiquity of plastic particles, and therefore the risk of sample contamination and their diverse properties (e.g., size, shape, composition), all adding to the complexity of identifying and quantifying them in biological matrices. To address these issues, we propose a framework that integrates orthogonal analytical techniques to enhance the data reliability. Commonly used analytical techniques for the analysis of micro- and nanoplastics are assigned a category based on their specificity when identifying plastic particles. The framework proposes minimum data requirements from orthogonal techniques for the identification of plastic particles at various confidence levels. Clear communication of analytical confidence is vital, and we present a structured approach to support this. We emphasize the importance of scientific integrity, rigorous study design, and transparent reporting in health research. Finally, we call for the universal adoption of harmonized confidence criteria for reporting the presence of plastics in humans, an essential step toward informed decision-making.

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